AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites
Phishing is a type of social web-engineering attack in cyberspace where criminals steal valuable data or information from insensitive or uninformed users of the internet. Existing countermeasures in the form of anti-phishing software and computational methods for detecting phishing activities have p...
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| Main Authors: | , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers Inc.
2020
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090039023&doi=10.1109%2fACCESS.2020.3013699&partnerID=40&md5=1c82ce8aeb152f3cc4c575ab106fd318 http://eprints.utp.edu.my/23383/ |
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| Summary: | Phishing is a type of social web-engineering attack in cyberspace where criminals steal valuable data or information from insensitive or uninformed users of the internet. Existing countermeasures in the form of anti-phishing software and computational methods for detecting phishing activities have proven to be effective. However, new methods are deployed by hackers to thwart these countermeasures. Due to the evolving nature of phishing attacks, the need for novel and efficient countermeasures becomes crucial as the effect of phishing attacks are often fatal and disastrous. Artificial Intelligence (AI) schemes have been the cornerstone of modern countermeasures used for mitigating phishing attacks. AI-based phishing countermeasures or methods possess their shortcomings particularly the high false alarm rate and the inability to interpret how most phishing methods perform their function. This study proposed four (4) meta-learner models (AdaBoost-Extra Tree (ABET), Bagging-Extra tree (BET), Rotation Forest-Extra Tree (RoFBET) and LogitBoost-Extra Tree (LBET)) developed using the extra-tree base classifier. The proposed AI-based meta-learners were fitted on phishing website datasets (currently with the newest features) and their performances were evaluated. The models achieved a detection accuracy not lower than 97 with a drastically low false-positive rate of not more 0.028. In addition, the proposed models outperform existing ML-based models in phishing attack detection. Hence, we recommend the adoption of meta-learners when building phishing attack detection models. © 2013 IEEE. |
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